

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


a__												=						{
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

a__												=						AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')



def   __UpperCAmelCase					(      __a :     Any						,__a :     List[str]=False      )     ->	List[Any]:

					"""simple docstring"""

					_a ,   _a      :       List[Any]   =						create_model(
					    '''HTSAT-tiny'''						,'''roberta'''						,__a						,precision='''fp32'''						,device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''						,enable_fusion=__a						,fusion_type='''aff_2d''' if enable_fusion else None						,)
					return model, model_cfg



def   __UpperCAmelCase					(      __a :     str      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Any   =						{}

					_a      :       int   =						R'''.*sequential.(\d+).*'''
					_a      :       List[Any]   =						R'''.*_projection.(\d+).*'''

					for key, value in state_dict.items():
										# check if any key needs to be modified
										for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
															if key_to_modify in key:
																				_a      :       List[Any]   =						key.replace(__a						,__a      )

										if re.match(__a						,__a      ):
															# replace sequential layers with list
															_a      :       int   =						re.match(__a						,__a      ).group(1      )

															_a      :       List[str]   =						key.replace(F"""sequential.{sequential_layer}."""						,F"""layers.{int(__a      )//3}.linear."""      )
										elif re.match(__a						,__a      ):
															_a      :       Optional[Any]   =						int(re.match(__a						,__a      ).group(1      )      )

															# Because in CLAP they use `nn.Sequential`...
															_a      :       Optional[int]   =						1 if projecton_layer == 0 else 2

															_a      :       List[str]   =						key.replace(F"""_projection.{projecton_layer}."""						,F"""_projection.linear{transformers_projection_layer}."""      )

										if "audio" and "qkv" in key:
															# split qkv into query key and value
															_a      :       List[str]   =						value
															_a      :       Union[str, Any]   =						mixed_qkv.size(0      ) // 3

															_a      :       Tuple   =						mixed_qkv[:qkv_dim]
															_a      :       Dict   =						mixed_qkv[qkv_dim : qkv_dim * 2]
															_a      :       Dict   =						mixed_qkv[qkv_dim * 2 :]

															_a      :       Optional[int]   =						query_layer
															_a      :       Tuple   =						key_layer
															_a      :       Tuple   =						value_layer
										else:
															_a      :       Tuple   =						value

					return model_state_dict



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Optional[int]						,__a :     Dict						,__a :     Dict=False      )     ->	Any:

					"""simple docstring"""

					_a ,   _a      :       Optional[Any]   =						init_clap(__a						,enable_fusion=__a      )

					clap_model.eval()
					_a      :       Tuple   =						clap_model.state_dict()
					_a      :       Union[str, Any]   =						rename_state_dict(__a      )

					_a      :       Union[str, Any]   =						ClapConfig()
					_a      :       Dict   =						enable_fusion
					_a      :       Dict   =						ClapModel(__a      )

					# ignore the spectrogram embedding layer
					model.load_state_dict(__a						,strict=__a      )

					model.save_pretrained(__a      )
					transformers_config.save_pretrained(__a      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
			parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
			parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
			a__												=						parser.parse_args()

			convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)











"""simple docstring"""


import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			None
						if token is not None:
												lowerCAmelCase   :List[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Dict						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						lowerCAmelCase   :Optional[Any]						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :Tuple						=			{}

						try:
												job_links.update({job['name']: job['html_url'] for job in result['jobs']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		job_links.update({job['name']: job['html_url'] for job in result['jobs']} )

												return job_links
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Any						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Optional[int]						=			F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
						lowerCAmelCase   :Any						=			requests.get(a__     ,      headers=a__ ).json()
						lowerCAmelCase   :str						=			{}

						try:
												artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )
												lowerCAmelCase   :List[Any]						=			math.ceil((result['total_count'] - 1_00) / 1_00 )

												for i in range(a__ ):
																		lowerCAmelCase   :List[Any]						=			requests.get(url + F"""&page={i + 2}"""     ,      headers=a__ ).json()
																		artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']} )

												return artifacts
						except Exception:
												print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )

						return {}


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			None
						if token is not None:
												lowerCAmelCase   :Optional[Any]						=			{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

						lowerCAmelCase   :Tuple						=			requests.get(a__     ,      headers=a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Optional[int]						=			result.headers['Location']
						lowerCAmelCase   :int						=			requests.get(a__     ,      allow_redirects=a__ )
						lowerCAmelCase   :Union[str, Any]						=			os.path.join(a__     ,      F"""{artifact_name}.zip""" )
						with open(a__     ,      'wb' ) as fp:
												fp.write(response.content )


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			[]
						lowerCAmelCase   :Dict						=			[]
						lowerCAmelCase   :Optional[Any]						=			None

						with zipfile.ZipFile(a__ ) as z:
												for filename in z.namelist():
																		if not os.path.isdir(a__ ):
																								# read the file
																								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																														with z.open(a__ ) as f:
																																				for line in f:
																																										lowerCAmelCase   :int						=			line.decode('UTF-8' ).strip()
																																										if filename == "failures_line.txt":
																																																try:
																																																						# `error_line` is the place where `error` occurs
																																																						lowerCAmelCase   :int						=			line[: line.index(': ' )]
																																																						lowerCAmelCase   :int						=			line[line.index(': ' ) + len(': ' ) :]
																																																						errors.append([error_line, error] )
																																																except Exception:
																																																						# skip un-related lines
																																																						pass
																																										elif filename == "summary_short.txt" and line.startswith('FAILED ' ):
																																																# `test` is the test method that failed
																																																lowerCAmelCase   :int						=			line[len('FAILED ' ) :]
																																																failed_tests.append(a__ )
																																										elif filename == "job_name.txt":
																																																lowerCAmelCase   :List[str]						=			line

						if len(a__ ) != len(a__ ):
												raise ValueError(
												    F"""`errors` and `failed_tests` should have the same number of elements. Got {len(a__ )} for `errors` """
												    F"""and {len(a__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
												    ' problem.' )

						lowerCAmelCase   :Optional[int]						=			None
						if job_name and job_links:
												lowerCAmelCase   :Dict						=			job_links.get(a__     ,      a__ )

						# A list with elements of the form (line of error, error, failed test)
						lowerCAmelCase   :Union[str, Any]						=			[x + [y] + [job_link] for x, y in zip(a__     ,      a__ )]

						return result


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			[]

						lowerCAmelCase   :Optional[int]						=			[os.path.join(a__     ,      a__ ) for p in os.listdir(a__ ) if p.endswith('.zip' )]
						for p in paths:
												errors.extend(get_errors_from_single_artifact(a__     ,      job_links=a__ ) )

						return errors


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Counter()
						counter.update([x[1] for x in logs] )
						lowerCAmelCase   :Tuple						=			counter.most_common()
						lowerCAmelCase   :Dict						=			{}
						for error, count in counts:
												if error_filter is None or error not in error_filter:
																		lowerCAmelCase   :Optional[int]						=			{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

						lowerCAmelCase   :Optional[Any]						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			test.split('::' )[0]
						if test.startswith('tests/models/' ):
												lowerCAmelCase   :Union[str, Any]						=			test.split('/' )[2]
						else:
												lowerCAmelCase   :Optional[int]						=			None

						return test


def        UpperCAmelCase      (					a__     ,      a__=None ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[(x[0], x[1], get_model(x[2] )) for x in logs]
						lowerCAmelCase   :Any						=			[x for x in logs if x[2] is not None]
						lowerCAmelCase   :Tuple						=			{x[2] for x in logs}

						lowerCAmelCase   :Optional[Any]						=			{}
						for test in tests:
												lowerCAmelCase   :Union[str, Any]						=			Counter()
												# count by errors in `test`
												counter.update([x[1] for x in logs if x[2] == test] )
												lowerCAmelCase   :List[str]						=			counter.most_common()
												lowerCAmelCase   :str						=			{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
												lowerCAmelCase   :Optional[Any]						=			sum(error_counts.values() )
												if n_errors > 0:
																		lowerCAmelCase   :Dict						=			{'count': n_errors, 'errors': error_counts}

						lowerCAmelCase   :Dict						=			dict(sorted(r.items()     ,      key=lambda a__ : item[1]["count"]     ,      reverse=a__ ) )
						return r


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| no. | error | status |'
						lowerCAmelCase   :Optional[int]						=			'|-:|:-|:-|'
						lowerCAmelCase   :Tuple						=			[header, sep]
						for error in reduced_by_error:
												lowerCAmelCase   :Optional[Any]						=			reduced_by_error[error]['count']
												lowerCAmelCase   :List[str]						=			F"""| {count} | {error[:1_00]} |  |"""
												lines.append(a__ )

						return "\n".join(a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Any						=			'| model | no. of errors | major error | count |'
						lowerCAmelCase   :int						=			'|-:|-:|-:|-:|'
						lowerCAmelCase   :int						=			[header, sep]
						for model in reduced_by_model:
												lowerCAmelCase   :Dict						=			reduced_by_model[model]['count']
												lowerCAmelCase	, lowerCAmelCase   :Any						=			list(reduced_by_model[model]['errors'].items() )[0]
												lowerCAmelCase   :Any						=			F"""| {model} | {count} | {error[:60]} | {_count} |"""
												lines.append(a__ )

						return "\n".join(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
		parser.add_argument(
		    '--output_dir',
		    type=str,
		    required=True,
		    help='Where to store the downloaded artifacts and other result files.',
		)
		parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		__SCREAMING_SNAKE_CASE   =   get_job_links(args.workflow_run_id, token=args.token)
		__SCREAMING_SNAKE_CASE   =   {}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								__SCREAMING_SNAKE_CASE   =   k.find(' / ')
								__SCREAMING_SNAKE_CASE   =   k[index + len(' / ') :]
						__SCREAMING_SNAKE_CASE   =   v
		with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		__SCREAMING_SNAKE_CASE   =   get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		__SCREAMING_SNAKE_CASE   =   Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		__SCREAMING_SNAKE_CASE   =   counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		__SCREAMING_SNAKE_CASE   =   reduce_by_error(errors)
		__SCREAMING_SNAKE_CASE   =   reduce_by_model(errors)

		__SCREAMING_SNAKE_CASE   =   make_github_table(reduced_by_error)
		__SCREAMING_SNAKE_CASE   =   make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
				fp.write(sa)